Evaluation for flow estimation.
As illustrated previously, our flow estimator is modified from PWC-Lite [1]}, which is a lightweight model of the state-of-the-art method PWC-Net [2]}. To show the effectiveness of our adaptations, we compare our flow estimator with these two approaches. Since our work aims at frame interpolation issue, we just make comparisons by the inference loss via Equation (2) for reference, during the designing stage of our flow-estimator. It is worth to mention that we also provide the result of the fine-tuned PWC-Net, which outperforms the original PWC-Net. It turns out that the original models for flow estimation are trained for color images. Therefore, for the final evaluations of our entire model, instead of using the weights of pre-trained models under comparison, we fine-tune all the methods using our training dataset.
